pybullet

name	pybullet
description	PyBullet 物理仿真开发技能 - Python 机器人仿真、强化学习环境、GPU 加速、URDF 导入
argument-hint	pybullet仿真 OR 强化学习环境 OR 机器人仿真 OR GPU加速
user-invocable	true

PyBullet Physics Simulation Skill

用于 PyBullet 物理仿真环境的配置和开发

---

何时使用

当需要以下帮助时使用此技能：

• 安装和配置 PyBullet
• 创建机器人仿真环境
• 编写强化学习训练环境
• 导入 URDF/SDF 模型
• 使用 GPU 加速仿真

---

快速参考

安装 PyBullet

pip install pybullet
pip install pybullet-gym  # 预定义环境

# GPU 版本 (需要 CUDA)
pip install pybullet-gpu

---

基本使用

连接模式

import pybullet as p
import pybullet_data

# GUI 模式 - 可视化
client_id = p.connect(p.GUI)
# 或
client_id = p.connect(p.GUI, options="--width=1920 --height=1080")

# DIRECT 模式 - 无窗口，快速仿真
client_id = p.connect(p.DIRECT)

# SHARED_MEMORY - 多进程共享
client_id = p.connect(p.SHARED_MEMORY)

# 加载数据路径
p.setAdditionalSearchPath(pybullet_data.getDataPath())

基本仿真循环

import pybullet as p
import time

# 连接
p.connect(p.GUI)

# 设置重力
p.setGravity(0, 0, -9.81)

# 设置时间步
p.setTimeStep(1/240)

# 加载地面
plane_id = p.loadURDF("plane.urdf")

# 加载机器人
robot_id = p.loadURDF("franka_panda/panda.urdf", [0, 0, 0])

# 仿真循环
for _ in range(1000):
    p.stepSimulation()
    time.sleep(1/240)

# 断开连接
p.disconnect()

---

机器人模型

加载 URDF

# 基本加载
robot_id = p.loadURDF(
    "robot.urdf",
    basePosition=[0, 0, 0.5],
    baseOrientation=[0, 0, 0, 1],
    useFixedBase=True
)

# 获取关节信息
num_joints = p.getNumJoints(robot_id)
for i in range(num_joints):
    info = p.getJointInfo(robot_id, i)
    print(f"Joint {i}: {info[1].decode()}")

# 读取状态
joint_states = p.getJointStates(robot_id, [0, 1, 2])
positions = [state[0] for state in joint_states]
velocities = [state[1] for state in joint_states]

创建简单机器人

# 创建四轮车
def create_mobile_robot():
    # 车身
    body_id = p.createMultiBody(
        baseMass=1.0,
        baseCollisionShape=p.createCollisionShape(p.GEOM_BOX, halfExtents=[0.2, 0.1, 0.05]),
        baseVisualShape=p.createVisualShape(p.GEOM_BOX, halfExtents=[0.2, 0.1, 0.05], rgbaColor=[0.3, 0.3, 0.8, 1]),
        basePosition=[0, 0, 0.1]
    )
    
    # 轮子
    wheel_radius = 0.05
    wheel_positions = [
        [0.15, 0.12, 0.05],
        [0.15, -0.12, 0.05],
        [-0.15, 0.12, 0.05],
        [-0.15, -0.12, 0.05]
    ]
    
    wheel_ids = []
    for pos in wheel_positions:
        wheel_id = p.createMultiBody(
            baseMass=0.1,
            baseCollisionShape=p.createCollisionShape(p.GEOM_CYLINDER, radius=wheel_radius, height=0.02),
            baseVisualShape=p.createVisualShape(p.GEOM_CYLINDER, radius=wheel_radius, length=0.02, rgbaColor=[0.1, 0.1, 0.1, 1]),
            basePosition=pos,
            baseOrientation=[0, 0, 0, 1]
        )
        wheel_ids.append(wheel_id)
    
    return body_id, wheel_ids


 ():
    links = []
    parent = -
    
    
    base_id = p.createMultiBody(
        baseMass=,
        baseCollisionShape=p.createCollisionShape(p.GEOM_BOX, halfExtents=[, , ]),
        basePosition=[, , ]
    )
    links.append(base_id)
    parent = base_id
    
    
    link_params = [
        (, , [, , ]),
        (, , [, , ]),
        (, , [, , ])
    ]
    
     mass, radius, pos  link_params:
        link_id = p.createMultiBody(
            baseMass=mass,
            baseCollisionShape=p.createCollisionShape(p.GEOM_CYLINDER, radius=radius, height=),
            baseVisualShape=p.createVisualShape(p.GEOM_CYLINDER, radius=radius, length=, rgbaColor=[, , , ]),
            basePosition=pos,
            baseInertialFramePosition=[, , ],
            baseInertialFrameOrientation=[, , , ]
        )
        
        
        p.createConstraint(
            parent, -, link_id, -,
            jointType=p.JOINT_REVOLUTE,
            jointAxis=[, , ],
            parentFramePosition=[, , ],
            childFramePosition=[, , -]
        )
        
        links.append(link_id)
        parent = link_id
    
     links

---

控制器

位置控制

# 位置控制
def position_control(robot_id, joint_indices, target_positions):
    for i, target in zip(joint_indices, target_positions):
        p.setJointMotorControl2(
            bodyUniqueId=robot_id,
            jointIndex=i,
            controlMode=p.POSITION_CONTROL,
            targetPosition=target,
            force=100
        )

# 使用
position_control(robot_id, [1, 2, 3], [0.5, -0.3, 0.8])

速度控制

# 速度控制
def velocity_control(robot_id, joint_indices, velocities):
    for i, vel in zip(joint_indices, velocities):
        p.setJointMotorControl2(
            bodyUniqueId=robot_id,
            jointIndex=i,
            controlMode=p.VELOCITY_CONTROL,
            targetVelocity=vel,
            force=50
        )

力控制

# 力控制
def torque_control(robot_id, joint_indices, torques):
    for i, torque in zip(joint_indices, torques):
        p.setJointMotorControl2(
            bodyUniqueId=robot_id,
            jointIndex=i,
            controlMode=p.TORQUE_CONTROL,
            force=torque
        )

# 启用零重力模式 (用于位置控制)
p.setJointMotorControl2(
    bodyUniqueId=robot_id,
    jointIndex=1,
    controlMode=p.POSITION_CONTROL,
    targetPosition=0,
    force=0,
    positionGain=0,
    velocityGain=0,
    maxVelocity=10
)

PID 控制

class PIDController:
    def __init__(self, kp=1.0, ki=0.1, kd=0.5):
        self.kp = kp
        self.ki = ki
        self.kd = kd
        self.integral = 0
        self.prev_error = 0
        
    def compute(self, target, current, dt):
        error = target - current
        self.integral += error * dt
        derivative = (error - self.prev_error) / dt
        
        output = self.kp * error + self.ki * self.integral + self.kd * derivative
        self.prev_error = error
        
        return output

---

传感器

获取传感器数据

# 关节状态
states = p.getJointStates(robot_id, range(num_joints))
positions = [s[0] for s in states]
velocities = [s[1] for s in states]

# 基础状态
base_pos, base_orn = p.getBasePositionAndOrientation(robot_id)
base_vel, base_ang = p.getBaseVelocity(robot_id)

# 相机图像
import cv2
import numpy as np

# 渲染图像
width = 640
height = 480
img = p.getCameraImage(width, height, renderer=p.ER_BULLET_HARDWARE_OPENGL)
rgb_image = np.array(img[2], dtype=np.uint8).reshape(height, width, 4)
rgb_image = rgb_image[:, :, :3]
rgb_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)

深度传感器

# 深度图像
img = p.getCameraImage(
    width, height,
    viewMatrix=view_mat,
    projectionMatrix=proj_mat,
    renderer=p.ER_BULLET_HARDWARE_OPENGL
)
depth_buffer = np.array(img[3])
depth_image = far_near / (1 - depth_buffer * (1 - far_near))

激光雷达模拟

# 手动实现激光雷达
def get_lidar_data(robot_id, num_rays=360, max_range=10):
    results = []
    base_pos, base_orn = p.getBasePositionAndOrientation(robot_id)
    euler = p.getEulerFromQuaternion(base_orn)
    yaw = euler[2]
    
    for i in range(num_rays):
        angle = yaw + (2 * np.pi * i / num_rays)
        direction = [np.cos(angle), np.sin(angle), 0]
        
        ray_from = base_pos
        ray_to = [
            base_pos[0] + direction[0] * max_range,
            base_pos[1] + direction[1] * max_range,
            base_pos[2] + direction[2] * max_range
        ]
        
        ray_result = p.rayTestBatch([ray_from], [ray_to])
        if ray_result[0][0] != -1:
            results.append(ray_result[0][2])
        else:
            results.append(max_range)
    
    return results

---

强化学习环境

创建 Gym 环境

import gym
from gym import spaces
import numpy as np

class RobotEnv(gym.Env):
    def __init__(self):
        super().__init__()
        
        # 连接 PyBullet
        self.client = p.connect(p.DIRECT)
        
        # 动作空间
        self.action_space = spaces.Box(
            low=-1, high=1, shape=(4,), dtype=np.float32
        )
        
        # 观察空间
        self.observation_space = spaces.Box(
            low=-np.inf, high=np.inf, shape=(12,), dtype=np.float32
        )
        
    def reset(self):
        p.resetSimulation()
        p.setGravity(0, 0, -9.81)
        
        # 加载环境
        plane = p.loadURDF("plane.urdf")
        self.robot = p.loadURDF("robot.urdf", [0, 0, 0.5])
        
        return self._get_obs()
    
    def step(self, action):
        # 应用动作
        for i, joint in enumerate(range(1, 5)):
            p.setJointMotorControl2(
                .robot, joint,
                p.POSITION_CONTROL,
                targetPosition=action[i],
                force=
            )
        
        p.stepSimulation()
        
        obs = ._get_obs()
        reward = ._compute_reward()
        done = ._is_done()
        
         obs, reward, done, {}
    
     ():
        states = p.getJointStates(.robot, (, ))
         np.array([s[]  s  states] + [s[]  s  states])
    
     ():
        
         
    
     ():
         
    
     ():
        p.disconnect()

使用 Stable-Baselines3

from stable_baselines3 import PPO
from stable_baselines3.common.env_checker import check_env

# 检查环境
check_env(RobotEnv())

# 创建环境
env = RobotEnv()

# 创建模型
model = PPO("MlpPolicy", env, verbose=1)

# 训练
model.learn(total_timesteps=100000)

# 保存
model.save("ppo_robot")

---

GPU 加速

GPU 渲染

# 使用 GPU 渲染
p.connect(p.GUI, options="--background_color_red=0.8 --background_color_green=0.8 --background_color_blue=0.8")

# 或在 DIRECT 模式下使用 GPU
p.connect(p.DIRECT, options="--gpu")

并行环境

# 启用多线程
import multiprocessing as mp

def run_env(env_id):
    import pybullet as p
    p.connect(p.DIRECT)
    # 仿真...
    return result

with mp.Pool(4) as pool:
    results = pool.map(run_env, range(4))

---

碰撞检测

# 检测碰撞
contact_points = p.getContactPoints(bodyA=robot_id, bodyB=plane_id)

if contact_points:
    for contact in contact_points:
        normal_force = contact[9]
        print(f"Normal force: {normal_force}")

# 设置碰撞过滤
p.setCollisionFilterGroupMask(
    bodyUniqueId=robot_id,
    linkIndex=-1,
    collisionFilterGroup=1,
    collisionFilterMask=1
)

---

常见问题

问题 1: 仿真不稳定

解决方案：

• 减小时间步长 (1/480 或 1/960)
• 增加迭代次数
• 调整约束公差

问题 2: 机器人穿模

解决方案：

• 启用连续碰撞检测 (CCD)
• 增加碰撞层精度
• 使用更厚的碰撞体

问题 3: GPU 内存不足

解决方案：

• 减少并行环境数
• 降低渲染分辨率
• 使用 DIRECT 模式

---

相关资源

• PyBullet 文档
• PyBullet Gym
• Bullet Physics

---

另见

• rl-environments - RL 环境
• physics-simulation - 物理仿真
• robot-control - 机器人控制
• mujoco - MuJoCo 仿真
• gazebo-harmonic - Gazebo 仿真